Overview

Dataset statistics

Number of variables14
Number of observations266
Missing cells844
Missing cells (%)22.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory225.9 B

Variable types

Categorical2
Unsupported2
Numeric10

Alerts

Country Name has a high cardinality: 266 distinct values High cardinality
Country Code has a high cardinality: 266 distinct values High cardinality
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
1990 has 266 (100.0%) missing values Missing
2000 has 34 (12.8%) missing values Missing
2011 has 29 (10.9%) missing values Missing
2012 has 30 (11.3%) missing values Missing
2013 has 31 (11.7%) missing values Missing
2014 has 31 (11.7%) missing values Missing
2015 has 31 (11.7%) missing values Missing
2016 has 32 (12.0%) missing values Missing
2017 has 31 (11.7%) missing values Missing
2018 has 31 (11.7%) missing values Missing
2019 has 32 (12.0%) missing values Missing
2020 has 266 (100.0%) missing values Missing
Country Name is uniformly distributed Uniform
Country Code is uniformly distributed Uniform
Country Name has unique values Unique
Country Code has unique values Unique
1990 is an unsupported type, check if it needs cleaning or further analysis Unsupported
2020 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-04-02 20:13:57.146836
Analysis finished2022-04-02 20:14:11.998926
Duration14.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Afghanistan
 
1
St. Lucia
 
1
Serbia
 
1
Seychelles
 
1
Sierra Leone
 
1
Other values (261)
261 

Length

Max length52
Median length9
Mean length12.40225564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.4%
St. Lucia1
 
0.4%
Serbia1
 
0.4%
Seychelles1
 
0.4%
Sierra Leone1
 
0.4%
Singapore1
 
0.4%
Sint Maarten (Dutch part)1
 
0.4%
Slovak Republic1
 
0.4%
Slovenia1
 
0.4%
Solomon Islands1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T15:14:12.089712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20
 
4.0%
and12
 
2.4%
income11
 
2.2%
ida10
 
2.0%
africa9
 
1.8%
islands9
 
1.8%
asia8
 
1.6%
ibrd8
 
1.6%
middle7
 
1.4%
rep7
 
1.4%
Other values (310)404
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
AFG
 
1
LCA
 
1
SRB
 
1
SYC
 
1
SLE
 
1
Other values (261)
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowASM
5th rowAND

Common Values

ValueCountFrequency (%)
AFG1
 
0.4%
LCA1
 
0.4%
SRB1
 
0.4%
SYC1
 
0.4%
SLE1
 
0.4%
SGP1
 
0.4%
SXM1
 
0.4%
SVK1
 
0.4%
SVN1
 
0.4%
SLB1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T15:14:12.196398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg1
 
0.4%
bhr1
 
0.4%
cpv1
 
0.4%
bdi1
 
0.4%
dza1
 
0.4%
asm1
 
0.4%
and1
 
0.4%
ago1
 
0.4%
atg1
 
0.4%
arg1
 
0.4%
Other values (256)256
96.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1990
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

2000
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct229
Distinct (%)98.7%
Missing34
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean279.8779907
Minimum0.57049705
Maximum2415.328507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:12.301146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.57049705
5-th percentile2.457207011
Q17.65804855
median48.13419927
Q3216.3881864
95-th percentile1563.885618
Maximum2415.328507
Range2414.75801
Interquartile range (IQR)208.7301378

Descriptive statistics

Standard deviation525.0406831
Coefficient of variation (CV)1.875962743
Kurtosis5.141387448
Mean279.8779907
Median Absolute Deviation (MAD)44.20960477
Skewness2.423357841
Sum64931.69385
Variance275667.7189
MonotonicityNot monotonic
2022-04-02T15:14:12.437343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.520380312
 
0.8%
10.721207842
 
0.8%
4.1180532612
 
0.8%
45.224612231
 
0.4%
192.23729231
 
0.4%
23.445209331
 
0.4%
727.29733531
 
0.4%
90.604149551
 
0.4%
178.04450021
 
0.4%
568.73398251
 
0.4%
Other values (219)219
82.3%
(Missing)34
 
12.8%
ValueCountFrequency (%)
0.570497051
0.4%
0.795110981
0.4%
1.189072511
0.4%
1.239698191
0.4%
1.326670731
0.4%
1.476450481
0.4%
1.684797631
0.4%
1.999059891
0.4%
2.219825361
0.4%
2.228220231
0.4%
ValueCountFrequency (%)
2415.3285071
0.4%
2408.7608671
0.4%
2315.5436171
0.4%
2204.3392121
0.4%
2074.3991341
0.4%
2017.0034561
0.4%
1962.6418331
0.4%
1835.8171631
0.4%
1833.3879331
0.4%
1682.8615041
0.4%

2011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct234
Distinct (%)98.7%
Missing29
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean681.0019943
Minimum1.2888907
Maximum7513.967795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:12.567285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.2888907
5-th percentile5.35600152
Q133.40147743
median158.9866734
Q3503.6260375
95-th percentile3757.639513
Maximum7513.967795
Range7512.678904
Interquartile range (IQR)470.2245601

Descriptive statistics

Standard deviation1256.937454
Coefficient of variation (CV)1.845717728
Kurtosis6.762716781
Mean681.0019943
Median Absolute Deviation (MAD)149.1899519
Skewness2.585040011
Sum161397.4726
Variance1579891.762
MonotonicityNot monotonic
2022-04-02T15:14:12.695506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.26904322
 
0.8%
36.34236462
 
0.8%
12.628398632
 
0.8%
2.850960251
 
0.4%
363.05351421
 
0.4%
162.30435161
 
0.4%
322.89372311
 
0.4%
50.614765191
 
0.4%
2137.7867241
 
0.4%
1552.127791
 
0.4%
Other values (224)224
84.2%
(Missing)29
 
10.9%
ValueCountFrequency (%)
1.28889071
0.4%
1.370623231
0.4%
2.293631741
0.4%
2.850960251
0.4%
3.176434541
0.4%
3.234395811
0.4%
3.323208171
0.4%
3.985739981
0.4%
4.779902711
0.4%
4.953099251
0.4%
ValueCountFrequency (%)
7513.9677951
0.4%
6049.9129431
0.4%
5365.6167361
0.4%
5251.7748751
0.4%
4294.3006381
0.4%
4260.1844221
0.4%
3996.0991321
0.4%
3954.6286251
0.4%
3938.4560141
0.4%
3798.7665411
0.4%

2012
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct233
Distinct (%)98.7%
Missing30
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean670.2650874
Minimum2.233482
Maximum7602.645368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:12.828417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.233482
5-th percentile5.603837765
Q132.00027084
median161.257059
Q3531.5244526
95-th percentile3607.044266
Maximum7602.645368
Range7600.411886
Interquartile range (IQR)499.5241818

Descriptive statistics

Standard deviation1219.08964
Coefficient of variation (CV)1.818817156
Kurtosis7.027912887
Mean670.2650874
Median Absolute Deviation (MAD)150.595399
Skewness2.603050513
Sum158182.5606
Variance1486179.551
MonotonicityNot monotonic
2022-04-02T15:14:12.957335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110.12297542
 
0.8%
35.601380532
 
0.8%
12.410248262
 
0.8%
2.2334821
 
0.4%
340.42515111
 
0.4%
184.4869141
 
0.4%
325.03634681
 
0.4%
47.902694731
 
0.4%
1868.3978521
 
0.4%
1399.2084381
 
0.4%
Other values (223)223
83.8%
(Missing)30
 
11.3%
ValueCountFrequency (%)
2.2334821
0.4%
2.261462071
0.4%
3.21999831
0.4%
3.298315211
0.4%
3.522883881
0.4%
3.566725761
0.4%
4.802080461
0.4%
4.855439041
0.4%
5.272954121
0.4%
5.39187881
0.4%
ValueCountFrequency (%)
7602.6453681
0.4%
5267.2757231
0.4%
5042.710271
0.4%
5042.0047581
0.4%
4374.6214881
0.4%
4292.1335831
0.4%
4074.7312791
0.4%
4054.8350241
0.4%
3874.9378591
0.4%
3746.5812751
0.4%

2013
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean697.4826526
Minimum2.28264735
Maximum7857.194681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:13.092509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.28264735
5-th percentile6.363748261
Q134.17016068
median173.3058293
Q3600.4022295
95-th percentile3762.864651
Maximum7857.194681
Range7854.912034
Interquartile range (IQR)566.2320688

Descriptive statistics

Standard deviation1252.734034
Coefficient of variation (CV)1.796079128
Kurtosis7.139495329
Mean697.4826526
Median Absolute Deviation (MAD)162.1037076
Skewness2.598989039
Sum163908.4234
Variance1569342.561
MonotonicityNot monotonic
2022-04-02T15:14:13.227136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.70148272
 
0.8%
34.170160682
 
0.8%
12.108749332
 
0.8%
2.770862521
 
0.4%
62.422808151
 
0.4%
333.87310521
 
0.4%
62.514367381
 
0.4%
1867.2298031
 
0.4%
309.36245661
 
0.4%
1004.7417271
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
2.282647351
0.4%
2.453884111
0.4%
2.770862521
0.4%
3.713644971
0.4%
3.738111691
0.4%
3.757260571
0.4%
3.959664661
0.4%
4.653409111
0.4%
5.26288431
0.4%
5.365679921
0.4%
ValueCountFrequency (%)
7857.1946811
0.4%
5626.7131911
0.4%
5330.7148431
0.4%
5254.9936571
0.4%
4215.4037841
0.4%
4178.853241
0.4%
3939.882141
0.4%
3897.6280281
0.4%
3887.3339991
0.4%
3849.5718691
0.4%

2014
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean714.7074818
Minimum2.68126936
Maximum7776.915436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:13.365765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.68126936
5-th percentile7.356971692
Q134.237398
median182.6802192
Q3620.5475965
95-th percentile3787.145344
Maximum7776.915436
Range7774.234167
Interquartile range (IQR)586.3101985

Descriptive statistics

Standard deviation1274.341442
Coefficient of variation (CV)1.783025188
Kurtosis6.651435246
Mean714.7074818
Median Absolute Deviation (MAD)169.9954358
Skewness2.545959465
Sum167956.2582
Variance1623946.111
MonotonicityNot monotonic
2022-04-02T15:14:13.500782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.30666412
 
0.8%
33.819962242
 
0.8%
12.684783382
 
0.8%
2.926995371
 
0.4%
67.569244761
 
0.4%
364.96306411
 
0.4%
61.579135851
 
0.4%
1882.8012621
 
0.4%
305.42759261
 
0.4%
1022.8949171
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
2.681269361
0.4%
2.926995371
0.4%
3.018203241
0.4%
3.946761361
0.4%
4.001189951
0.4%
4.04345691
0.4%
4.814142961
0.4%
5.662126821
0.4%
5.848657151
0.4%
6.633344061
0.4%
ValueCountFrequency (%)
7776.9154361
0.4%
5550.8322741
0.4%
5516.7168441
0.4%
5371.9522461
0.4%
4531.4172731
0.4%
4444.2994921
0.4%
4087.8558211
0.4%
3916.2847191
0.4%
3911.2834051
0.4%
3871.9809281
0.4%

2015
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean646.5995305
Minimum1.72049046
Maximum6469.649538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:13.634958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.72049046
5-th percentile6.224630043
Q132.30690365
median179.1296584
Q3594.5969643
95-th percentile3278.00493
Maximum6469.649538
Range6467.929048
Interquartile range (IQR)562.2900607

Descriptive statistics

Standard deviation1127.252359
Coefficient of variation (CV)1.743354745
Kurtosis5.888108321
Mean646.5995305
Median Absolute Deviation (MAD)164.8218492
Skewness2.461256533
Sum151950.8897
Variance1270697.882
MonotonicityNot monotonic
2022-04-02T15:14:13.763615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.11972082
 
0.8%
32.327993622
 
0.8%
14.110933582
 
0.8%
3.046786671
 
0.4%
69.503858641
 
0.4%
362.48487781
 
0.4%
65.921259531
 
0.4%
1675.2321291
 
0.4%
287.53696681
 
0.4%
873.13841591
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
1.720490461
0.4%
3.046786671
0.4%
3.292274451
0.4%
3.30250131
0.4%
3.567521651
0.4%
4.459425461
0.4%
5.030074151
0.4%
5.381675141
0.4%
5.791523481
0.4%
5.792099481
0.4%
ValueCountFrequency (%)
6469.6495381
0.4%
4804.2922511
0.4%
4700.7087171
0.4%
4651.1285921
0.4%
4604.2608981
0.4%
4581.8529821
0.4%
3582.1867991
0.4%
3559.3368071
0.4%
3459.0592181
0.4%
3342.2683371
0.4%

2016
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct231
Distinct (%)98.7%
Missing32
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean663.6905002
Minimum2.41127521
Maximum6400.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:13.892244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.41127521
5-th percentile6.161716554
Q132.72534762
median177.2654968
Q3638.0792439
95-th percentile3413.045212
Maximum6400.7
Range6398.288725
Interquartile range (IQR)605.3538962

Descriptive statistics

Standard deviation1153.669822
Coefficient of variation (CV)1.738264781
Kurtosis5.59033444
Mean663.6905002
Median Absolute Deviation (MAD)162.0483114
Skewness2.427942461
Sum155303.5771
Variance1330954.059
MonotonicityNot monotonic
2022-04-02T15:14:14.017907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.95892872
 
0.8%
29.319611132
 
0.8%
15.217185422
 
0.8%
3.056287881
 
0.4%
70.477028681
 
0.4%
425.24985661
 
0.4%
65.318181761
 
0.4%
1700.6472751
 
0.4%
263.25000321
 
0.4%
940.00844371
 
0.4%
Other values (221)221
83.1%
(Missing)32
 
12.0%
ValueCountFrequency (%)
2.411275211
0.4%
2.598026061
0.4%
3.056287881
0.4%
3.385343461
0.4%
4.721826171
0.4%
4.860544111
0.4%
5.171530141
0.4%
5.396008361
0.4%
5.482544531
0.4%
5.676981421
0.4%
ValueCountFrequency (%)
6400.71
0.4%
4965.9381481
0.4%
4790.8671491
0.4%
4786.7118751
0.4%
4681.9116531
0.4%
4623.0043371
0.4%
4130.9459181
0.4%
3666.1205731
0.4%
3508.0677511
0.4%
3501.8248691
0.4%

2017
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean696.607828
Minimum1.93147854
Maximum6652.84943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:14.152576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.93147854
5-th percentile6.145249215
Q132.54549736
median190.7005537
Q3643.0133885
95-th percentile3491.724895
Maximum6652.84943
Range6650.917951
Interquartile range (IQR)610.4678911

Descriptive statistics

Standard deviation1208.586612
Coefficient of variation (CV)1.734959848
Kurtosis5.599003878
Mean696.607828
Median Absolute Deviation (MAD)174.4709034
Skewness2.431151855
Sum163702.8396
Variance1460681.598
MonotonicityNot monotonic
2022-04-02T15:14:14.278210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.75065372
 
0.8%
31.5495212
 
0.8%
17.443623582
 
0.8%
3.348260091
 
0.4%
72.692084521
 
0.4%
61.0110711
 
0.4%
1782.1439571
 
0.4%
2.227108471
 
0.4%
305.22740871
 
0.4%
939.91388341
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
1.931478541
0.4%
2.07497741
0.4%
2.227108471
0.4%
3.28625431
0.4%
3.348260091
0.4%
4.007453541
0.4%
4.226580051
0.4%
4.674141641
0.4%
4.90075991
0.4%
5.139116951
0.4%
ValueCountFrequency (%)
6652.849431
0.4%
5117.2364191
0.4%
5036.4935961
0.4%
4944.9850931
0.4%
4943.2784981
0.4%
4873.5217521
0.4%
4843.6783621
0.4%
3929.4354021
0.4%
3692.6574621
0.4%
3668.3431981
0.4%

2018
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean739.7635891
Minimum2.72140895
Maximum7089.272138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:14.408861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.72140895
5-th percentile6.032781164
Q131.86696163
median196.3500438
Q3694.1828161
95-th percentile3611.639302
Maximum7089.272138
Range7086.550729
Interquartile range (IQR)662.3158545

Descriptive statistics

Standard deviation1283.026584
Coefficient of variation (CV)1.734373796
Kurtosis5.605154139
Mean739.7635891
Median Absolute Deviation (MAD)180.5279396
Skewness2.429206669
Sum173844.4434
Variance1646157.215
MonotonicityNot monotonic
2022-04-02T15:14:14.536293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.88170062
 
0.8%
31.866961632
 
0.8%
17.267145482
 
0.8%
2.721408951
 
0.4%
75.074285371
 
0.4%
74.069913321
 
0.4%
1925.6992621
 
0.4%
2.914720141
 
0.4%
323.44125271
 
0.4%
1029.7240961
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
2.721408951
0.4%
2.792609141
0.4%
2.914720141
0.4%
3.229255781
0.4%
3.362266941
0.4%
3.742546671
0.4%
4.104681021
0.4%
4.444762061
0.4%
4.97339741
0.4%
5.377041091
0.4%
ValueCountFrequency (%)
7089.2721381
0.4%
5414.5936541
0.4%
5338.8179011
0.4%
5288.1118031
0.4%
5214.6602171
0.4%
5153.7267091
0.4%
5091.3724681
0.4%
4252.2410021
0.4%
4065.2351791
0.4%
3944.4872931
0.4%

2019
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct231
Distinct (%)98.7%
Missing32
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean744.4975842
Minimum1.8114012
Maximum6871.953907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:14:14.666512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.8114012
5-th percentile6.437676671
Q131.53103146
median208.6155398
Q3705.0702882
95-th percentile3709.711893
Maximum6871.953907
Range6870.142506
Interquartile range (IQR)673.5392567

Descriptive statistics

Standard deviation1277.18292
Coefficient of variation (CV)1.715496393
Kurtosis5.337899994
Mean744.4975842
Median Absolute Deviation (MAD)191.9183403
Skewness2.390572745
Sum174212.4347
Variance1631196.211
MonotonicityNot monotonic
2022-04-02T15:14:15.033892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.389393842
 
0.8%
19.257738062
 
0.8%
130.9001992
 
0.8%
5.388990461
 
0.4%
83.635436141
 
0.4%
75.884465721
 
0.4%
1914.6315151
 
0.4%
3.690573321
 
0.4%
321.23417241
 
0.4%
1057.76861
 
0.4%
Other values (221)221
83.1%
(Missing)32
 
12.0%
ValueCountFrequency (%)
1.81140121
0.4%
3.25388681
0.4%
3.690573321
0.4%
3.93203521
0.4%
3.990053161
0.4%
4.448414621
0.4%
4.899102681
0.4%
5.168501421
0.4%
5.388990461
0.4%
6.071315031
0.4%
ValueCountFrequency (%)
6871.9539071
0.4%
5552.600231
0.4%
5346.0722551
0.4%
5341.7469961
0.4%
5201.2373751
0.4%
5000.1485531
0.4%
4814.1362381
0.4%
4228.4780251
0.4%
4048.5341731
0.4%
3890.5143991
0.4%

2020
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

Interactions

2022-04-02T15:14:09.946453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:13:58.705706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.377758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.448909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.547840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.678946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.154071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.199919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:07.500315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.863753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:10.044191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:13:59.432511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.481507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.554840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.659510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.777655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.248847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.296689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:07.663387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.973431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:10.149909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:13:59.536289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.589192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.665507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.770245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.883395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.351562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.397705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:07.783596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.078580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:10.254653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:13:59.637526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.700916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.775208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.906285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.991105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.455278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.502824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:07.911256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.186292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:10.362365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:13:59.755234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.812593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.892437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.022999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:04.144672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.563020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.609539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.145628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.314515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:10.714652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:13:59.867940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.920337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.006155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.138692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:04.267375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.673696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.716282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.333144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.428030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:10.821338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:13:59.968621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.028579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.115867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.249938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:04.376053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.780410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.814020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.436867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.529755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:10.927056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.068613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.131308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.220199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.356575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:04.486779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.884699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.914721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.545577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.628515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:11.029335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.168319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.234455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.322931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.462498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:04.924585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.987443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:07.055505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.652292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.731549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:11.139625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:00.273066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:01.341170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:02.433628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:03.570237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:05.047357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:06.092176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:07.256966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:08.760003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:14:09.838742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-02T15:14:15.150580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-02T15:14:15.324116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-02T15:14:15.499645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-02T15:14:15.674152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-02T15:14:11.346178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-02T15:14:11.577101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-02T15:14:11.755591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-02T15:14:11.920165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Country NameCountry Code199020002011201220132014201520162017201820192020
0AfghanistanAFGNaNNaN2.8509602.2334822.7708632.9269953.0467873.0562883.3482602.7214095.388990NaN
1AlbaniaALBNaN30.825579104.124526103.797180113.417021118.457388107.598633115.212352120.758235148.436569NaNNaN
2AlgeriaDZANaN44.316653202.944655244.723577235.838739258.972463205.426791176.502810170.199512168.575678161.333285NaN
3American SamoaASMNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4AndorraANDNaN852.4205051759.5158731574.3758561627.3344961725.1407561549.6311941631.3866561789.8612131916.9845631906.859429NaN
5AngolaAGONaN7.56664479.07305779.73165389.07904971.29150251.43055941.98650352.91251836.73723229.403301NaN
6Antigua and BarbudaATGNaN296.500860395.111243409.411217398.871449469.043344450.943582434.764289421.226463485.821192444.297023NaN
7ArgentinaARGNaN387.921609728.916685854.263579904.133482841.4892021021.710455716.473833966.729638692.617431589.928233NaN
8ArmeniaARMNaN5.94520958.98201958.10586952.80022360.01179458.08499158.81064353.72709052.11013565.002513NaN
9ArubaABWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

Country NameCountry Code199020002011201220132014201520162017201820192020
256Post-demographic dividendPSTNaN1471.1412773218.9216413205.6650063214.5881673323.6248833184.9917083290.1897803395.8167923578.0986653646.793027NaN
257Pre-demographic dividendPRENaN3.87211817.77605216.20418917.28105817.50955616.02670614.09131916.57903916.50451417.265082NaN
258Small statesSSTNaN127.417359311.794862326.425982361.424615386.775298367.122290372.123316389.968494399.007416410.422654NaN
259South AsiaSASNaN4.11805312.62839912.41024812.10874912.68478314.11093415.21718517.44362417.26714519.257738NaN
260South Asia (IDA & IBRD)TSANaN4.11805312.62839912.41024812.10874912.68478314.11093415.21718517.44362417.26714519.257738NaN
261Sub-Saharan AfricaSSFNaN10.72120836.34236535.60138134.17016133.81996232.32799429.31961131.54952131.86696231.389394NaN
262Sub-Saharan Africa (excluding high income)SSANaN10.68604236.31247135.55950434.12891633.77789332.28581429.27169331.49985131.81340131.337567NaN
263Sub-Saharan Africa (IDA & IBRD countries)TSSNaN10.72120836.34236535.60138134.17016133.81996232.32799429.31961131.54952131.86696231.389394NaN
264Upper middle incomeUMCNaN47.190617220.521404239.839442264.679847275.672288266.540387254.748927281.488710297.753478309.201107NaN
265WorldWLDNaN275.003856600.189259602.080549610.230393628.052018599.022982609.644743632.019080661.223987671.102860NaN